Biology subjects

Waterfall, J. J.

Publications and source records attributed to Waterfall, J. J..

11 recordsLinked to original sources

bioRxiv2026

Oncogenic fusions induce an extensive cancer-restricted cryptic proteome in Ewing sarcoma

How tumors generate a cryptic "dark" proteome absent from healthy tissues, and whether it can be reversibly activated, remains unclear. In Ewing sarcoma, all tumors are driven by EWSR1::ETS fusions, making it a tractable model to study dark proteome activation. Integrating matched transcriptomes, translatomes and proteomes from 48 patient tumors with long-read RNA sequencing, single-cell Ribo-seq, proteomics and immunopeptidomics in cell line models, we show that EWSR1::FLI1 acts as a reversible switch recurrently inducing hundreds of cancer-specific neoproteins. Many arise from canonical coding regions via intragenic transcription start sites, generating truncated or out-of-frame neoproteins. We uncover a fusion-dependent increase in ribosomal readthrough into poly(A) tails, generating a stable 80-amino-acid TRPM4 neoprotein that, despite lacking a stop codon, is the most abundant tumor-specific microprotein across patients. Proteomic, immunopeptidomic, and immunofluorescence analyses validate neoproteins as tumor-restricted antigens, revealing a therapeutically actionable cancer dark proteome controlled by one oncogenic fusion.

cancer biology↗

bioRxiv2026

An alignment-last approach enables rapid transcriptomic biomarker discovery in large cohorts

Canonical transcriptomic analysis requires committing from the outset to a reference genome or transcriptome, which imposes a predefined feature set, usually annotated genes or isoforms. Alignment and annotation dilute the signal through feature-level aggregation, discard any sequence absent from the reference, and require reprocessing the entire dataset for each new question (mutations, fusions, transposable elements). Here, we introduce the alignment-last paradigm, in which the read becomes the unit of comparison across samples, and alignment is deferred to annotate only the relevant sequences. Querying the merome, a reference-free cohort k-mer index, with just a handful of reads (about 0.01% of a sample's) reveals the cohort's transcriptomic structure in bulk and single-cell data. At single-cell resolution, these reads outperform genes for cell classification and rediscover, without supervision, a transposable-element signature (VL30) of exhausted T cells. Finally, unsupervised read-level differential analysis recovers established lncRNA biomarkers; uncovers new prognostic transposable-element reads in adrenocortical carcinoma and sarcomas; and extracts signals even from reads that fail to align.

bioinformatics↗

bioRxiv2026

Specific killing of Ewing sarcoma by TCR-T cells targeting public neogene-encoded antigens

EWSR1::FLI1, the oncogenic chimeric transcription factor driving Ewing sarcoma (EwS)induces expression of exquisitely EwS-specific neogenes (Ew_NGs) through neomorphic binding and transcription activation at GGAA microsatellites in genomic regions that are silent in normal tissues. We show that peptides encoded by Ew_NGs are presented on HLA-I complexes on EwS cells. The cytokine secretion of CD8+ T cells specific for Ew_NG-encoded HLA-I-bound peptides is activated by all HLA-I-matched EwS cells but not by non-EwS cells. These T cells kill EwS cells in an HLA-I restricted manner. This cytotoxicity is dependent on the expression of EWSR1::FLI1 and of the corresponding Ew_NG. It can be reproduced by transduction of the TCR into donor T cells (TCR-T) which kill EwS cells in vivo. Moreover, we show that neither off target nor allogeneic activation are observed with TCR-T thus paving the way for cell therapy in relapsed/resistant EwS patients for which therapeutic options are very limited. Statement of significanceThe chimeric transcription factor EWSR1::FLI1 generates tumor-specific neogenes encoding neoantigen presented by the HLA-I molecules of Ewing cells. Neoantigen-specific CD8+ T-cell clones and engineered TCR-T cells can selectively recognize and kill EwS tumor cells in vitro and in vivo.

immunology↗

bioRxiv2025

Myeloid reprogramming by poly(I:C) recruits progenitor-exhausted CD8+ T cells and sensitizes rhabdoid tumors to PD-1 blockade

Immune exclusion remains a major barrier to effective immunotherapy in solid tumors. Given the abundance and plasticity of tumor-associated macrophages (TAMs) in many tumors, including pediatric tumors, we investigated whether TLR3 activation could reprogram them to facilitate immune access. Single-cell and spatial profiling in a mouse model of rhabdoid tumors showed that they are dominated by TLR3-expressing TAMs, whose depletion delays tumor growth. Treatment with the TLR3 agonist poly(I:C) promotes immune cell infiltration, including progenitor-exhausted CD8+ T cells, by multiple mechanisms including the reduction and reprogramming of immunosuppressive TAMs, promoting nitric-oxide production by peritumoral macrophages, and inducing CXCL9/10 production. Combined poly(I:C) and PD-1 blockade elicited durable, complete tumor rejection. Human macrophages from tumor biopsies showed conserved TLR3 responsiveness, underscoring translational potential. These findings uncover a mechanism by which TLR3-driven myeloid reprogramming transforms immune-excluded tumors into checkpoint-responsive ones, revealing a therapeutic path to overcome resistance to PD-1 blockade.

immunology↗

bioRxiv2025

Dual PD-L1/TIGIT blockade induces PNAd+ HEV-like vessels and CD62L+ lymphocyte recruitment, driving rhabdoid tumor rejection

Rhabdoid tumors (RTs) are aggressive pediatric malignancies with poor prognosis and limited immunotherapy options. Here, we investigate the therapeutic potential of combined PD-L1 (Programmed cell death ligand 1) and TIGIT (T cell immunoreceptor with Ig and ITIM domains) immune checkpoint blockade in RTs using a preclinical murine model that recapitulates key features of human ATRT (Atypical teratoid rhabdoid tumors) subtypes. Transcriptomic analyses of human and murine RTs reveal co-expression of TIGIT and PD-1 (Programmed cell death 1) pathway components and their ligands, particularly in immune-infiltrated subtypes, supporting a rationale for dual blockade. Combination therapy induces complete tumor regression, prolongs survival, and reprograms the tumor immune microenvironment by enriching CD62L naive and central memory T cells and promoting selective T-cell clonal expansion. Notably, dual blockade initiates PNAd (Peripheral node addressin) high endothelial venule (HEV)-like structures, associated with focal lymphocyte clustering and enhanced immune cell recruitment. These findings reveal a mechanistic link between vascular remodeling and immune infiltration and support dual TIGIT and PD-L1 inhibition as a promising immunotherapeutic strategy for RTs.

immunology↗

bioRxiv2025

Yomix: An Interactive Tool for the Exploration of Low-Dimensional Embeddings in Omics Data

SummaryIn the analysis of diverse omics data, a common and important preliminary step involves computing low-dimensional embeddings using techniques such as PCA, UMAP, t-SNE, or variational autoencoders. These embeddings provide a global overview of sample distributions and their relationships, often serving as the basis for formulating biological hypotheses. To facilitate rapid and intuitive exploration of such low-dimensional embeddings, we developed Yomix, an interactive omics-agnostic visualization and data exploration tool. Yomix enables users to flexibly define subsets of interest using a lasso selection tool, instantly compute their feature signatures, and compare their distributions. Yomix is a fast and efficient tool for interactive exploration of diverse omics datasets. Availability and ImplementationYomix and its documentation are publicly available at https://github.com/perrin-isir/yomix.

bioinformatics↗

bioRxiv2025

Common origin for effector and regulatory Follicular and Tissue-Adapted CD4+ T cells in Non-Small Cell Lung Cancer

Tumor-invaded lymph nodes (LNs) serve as critical hubs for anti-tumor immunity, yet their role in orchestrating immune responses remains poorly understood. Using integrated single-cell RNA sequencing, T cell receptor sequencing, and chromatin accessibility profiling, we analyzed CD4+ T cells from matched blood, tumor-invaded LNs, and tumors of treatment-naive non-small cell lung cancer patients. We identified distinct immunological landscapes across these compartments. Compared to blood, tumor-invaded LNs and tumors were enriched for follicular regulatory T cells (Treg-Tfr), conventional T cell subsets with Tfh-like characteristics (Tconv-Tfh and Tconv-CXCL13), and tissue-resident memory Tregs (Treg-Trm). These populations share a BATF-dependent transcriptional program that governs T-cell activation and tissue adaptation, while simultaneously engaging distinct, subset-specific regulatory networks. Integrative TCR-RNA analysis revealed that tumor-reactive, neoantigen-specific T cell clones were enriched within these subsets and demonstrated extensive LN-tumor clonal sharing, indicating active recirculation between compartments. Through clonal coupling analysis and trajectory inference, we uncovered that Treg-Tfr cells function as multipotent progenitors that bifurcate into tissue-resident Treg-Trm or into ex-Tregs adopting a Tfh-like CXCL13+ ewector phenotype. Remarkably, follicular CD4+ T subsets from LNs and tumor were transcriptionally and epigenetically similar and localized to analogous germinal center niches. These findings establish tumor-invaded LNs as functional extensions of the tumor microenvironment that generate and maintain tumor-reactive CD4+ lineages. The identification of tissue-resident Treg-Tfr plasticity reveals a critical developmental checkpoint that could be therapeutically targeted to redirect immunosuppressive programs toward anti-tumor ewector responses.

immunology↗

bioRxiv2025

pyALRA: python implementation of low-rank zero-preserving approximation of single cell RNA-seq

SummaryWe present pyALRA, an efficient python implementation of the ALRA R package conceived to impute drop out values using a low-rank zero-preserving approximation for single cell RNA-seq. This re-implementation achieves similar performance prediction using corresponding python methods and allows both speed and RAM consumption improvements. AvailabilitypyALRA is released as an open-source software under the MIT license. The source code is available on GitHub at https://github.com/alexandrelanau/pyALRA.

bioinformatics↗

bioRxiv2022

Community assessment of methods to deconvolve cellular composition from bulk gene expression

We evaluate deconvolution methods, which infer levels of immune infiltration from bulk expression of tumor samples, through a community-wide DREAM Challenge. We assess six published and 22 community-contributed methods using in vitro and in silico transcriptional profiles of admixed cancer and healthy immune cells. Several published methods predict most cell types well, though they either were not trained to evaluate all functional CD8+ T cell states or do so with low accuracy. Several community-contributed methods address this gap, including a deep learning-based approach, whose strong performance establishes the applicability of this paradigm to deconvolution. Despite being developed largely using immune cells from healthy tissues, deconvolution methods predict levels of tumor-derived immune cells well. Our admixed and purified transcriptional profiles will be a valuable resource for developing deconvolution methods, including in response to common challenges we observe across methods, such as sensitive identification of functional CD4+ T cell states.

bioinformatics↗

bioRxiv2022

Multi-omics comparison of malignant and normal uveal melanocytes reveals novel molecular features of uveal melanoma.

Uveal Melanoma (UM) is a rare cancer resulting from transformation of melanocytes residing in the uveal tract. Integrative analysis has identified four molecular and clinical subsets in UM. To improve our molecular understanding of UM we performed extensive multi-omics characterization comparing two aggressive UM patient-derived xenograft models with normal choroidal melanocytes, including DNA optical mapping, specific histone modifications, and DNA topology analysis by Hi-C. Our gene expression and cytogenetic analyses suggest that genomic instability is a hallmark of UM. We also identified a recurrent deletion in the BAP1 promoter resulting in loss of expression and associated with high risk of metastases in UM patients. Hi-C revealed chromatin topology changes associated with up-regulation of PRAME, an independent prognostic biomarker in UM and potential therapeutic target. Our findings illustrate how multi-omics approaches can improve the understanding of tumorigenesis and reveals two novel mechanisms of gene expression dysregulation in UM.

genomics↗

bioRxiv2020

Contribution of resident and circulating precursors to tumor-infiltrating CD8+ T cell populations in non-small cell lung cancer patients

Tumor-infiltrating lymphocytes (TILs) in general, and CD8+ TILs in particular, represent a favorable prognostic factor in non-small cell lung cancer (NSCLC). The tissue origin, regenerative capacities, and differentiation pathways of TIL subpopulations, however, remain poorly understood. Using a combination of single cell RNA and T cell receptor (TCR) sequencing, we investigate the functional organization of TIL populations in primary NSCLC. We identify two CD8+ TIL subpopulations expressing memory-like gene modules: one is also present in blood (circulating precursors) and the other one in juxta-tumor tissue (tissue resident precursors). In tumors, these two precursor populations converge through a unique transitional state into terminally differentiated cells, often referred to as dysfunctional or exhausted. Differentiation is associated with TCR expansion, and transition from precursor to late differentiated states correlates with intratumor T cell cycling. These results provide a coherent working model for TIL origin, filiation and functional organization in primary NSCLC.

immunology↗